F-measure (\(F_\beta \) ):
The probability map is binarized by a threshold \(T \in (0,1)\) , where pixels with a probability greater (smaller) than \(T\)  are categorized as imperfect or intact regions. For a single image, the score \(F_\beta \)  [1]} is often used as a fitness criterion to justify the chosen threshold. For a well-selected threshold, such F-measure is expected to reach the maximum. It is calculated as
\(F_\beta = (1+\beta ^2)\cdot \frac{ { p_T} \times {r_T}}{ {\beta ^2\times p_T}+ {r_T}},\) 
